@article {JRN55309, title = {Digital soil mapping to support California ecological site development}, volume = {90}, year = {2020}, pages = {27-28}, chapter = {Government Publication/Report}, abstract = {In late January 2020, soil and rangeland scientists from the Agricultural Research Service (ARS) joined conservation staff from the Santa Lucia Conservancy in Carmel, California, to describe and sample grassland soils of the Central California Coastal Range. Recently, ARS scientists entered into a partnership with the Santa Lucia Conservancy, which stewards an 18,000-acre preserve of protected natural lands, to provide expertise and advice on rangeland ecology, develop state-and transition models, and analyze grazing and landscape data to evaluate grassland and rangeland response to the conservancy{\textquoteright}s targeted grazing program. ARS cooperators used digital soil mapping techniques to sample and map soils classes across the preserve{\textquoteright}s grasslands. Prior to sampling, we applied the conditioned Latin Hypercube Sampling (cLHS) method, a random stratified procedure that chooses sampling locations based on a suite of soil-forming environmental variables. Using cLHS with a sampling number optimization algorithm and a cost constrained implementation, we identified 41 soil and vegetation sampling sites across the preserve{\textquoteright}s coastal prairie and oak savanna landscapes. Many stakeholders and cooperators joined the soil and vegetation field sampling, including scientists from NRCS (Genevieve Landucci, Templeton, California), Point Blue Conservation Science (Chelsea Carey, Petaluma, California), the California Conservation Corps, Americorps Watershed Stewards Program, and the Santa Lucia Conservancy. The next step is to analyze chemical and physical properties of the soil samples and conduct analysis of the conservancy{\textquoteright}s vegetation and grazing data. Our collaboration helps support the Santa Lucia Conservancy{\textquoteright}s science-based mission to enable data-driven assessments of grassland and rangeland conditions, including the development of best management practices that promote native herbaceous plants, reduce noxious weeds, control non-native vegetation, and prevent brush encroachment.}, author = {S. W. Salley} } @conference {JRN55163, title = {Mobile phone app for gardeners to access soil survey information}, booktitle = {Social Sciences}, volume = {PL-60}, year = {2019}, month = {06/10/2019}, abstract = {

Soil resource information can help gardeners better understand what is possible to grow on their land, and how they could improve it. However, the delivery of soil survey interpretations to the end-user is often complicated by soil map unit complexity where multiple classes are contained within a map unit and the subsequent human modification of these soil classes into urban lawns or gardens. Even in areas where human development has expanded into previously mapped areas, soils have been so modified that soil maps no longer provide correct information of soil resources. The Land Potential Knowledge System (LandPKS) is a modular mobile phone app developed to connect farmers, gardeners, and other land managers to US and global soil survey information based on location and user inputs. It is available free on the Google Play and iPhone App stores. LandPKS recently implemented a soil identification (SoilID) algorithm to match smartphone-based user data acquisition (e.g., GPS location, soil texture, and color by depth) through cloud-based computing to return soil component matching for any given point. While this approach has proven to reliably deliver soil class information to the user, it is ultimately limited to available soil concepts for that area{\textquoteright}s soil survey. Our presentation will describe recent advances from the LandPKS app related to identifying these modified soil resources. We will also demonstrate other LandPKS modules that allow users to plan and record management inputs and activities, and document changes in soil health.

}, author = {S. W. Salley and Jeffrey E. Herrick and Jonathan Maynard} } @article {JRN55136, title = {A comparison of soil texture-by-feel estimates: Implications for the citizen soil scientist}, journal = {Soil Science Society of America}, volume = {82}, year = {2018}, month = {08/2018}, pages = {1526-1537}, chapter = {1526}, abstract = {

Estimating soil texture is a fundamental practice universally applied by soil scientists to classify and understand the behavior, health, and management of soil systems. While the accuracy of both the soil texture class and the estimates of percent sand and clay is generally accepted when completed by trained soil scientists, similar estimates by {\textquotedblleft}citizen scientists{\textquotedblright} or inexperienced seasonal resource scientists are often questioned. We compared soil texture classes determined by texture-by-feel and laboratory analyses for 2 groups: professional soil scientists who contributed to the USDA-NRCS National Soil Characterization Database and seasonal field technicians working on rangeland inventory and assessment programs in the Western United States and Namibia. Texture accuracy was compared using a confusion matrix to evaluate classification accuracy based on the assumption that laboratory measurements were correct. Our results show that the professional soil scientists predicted the laboratory-determined texture class for 66\% of the samples. Accuracy for seasonal field technicians was between 27\% and 41\%. When a {\textquotedblleft}correct{\textquotedblright} prediction was defined to include texture classes adjacent to the laboratory-determined texture based on a standard USDA texture triangle, accuracy increased to 91\% for professionals and 71\% to 78\% for seasonal field technicians. These findings highlight the need to improve options for increasing the accuracy of field-textured estimates for all soil texture observers, with relevance to career soil scientists, seasonal technicians, and citizen scientists. Opportunities for improving soil texture accuracy include training, calibration, and decision support tools that go beyond simple dichotomous keys.

}, doi = {doi:10.2136/sssaj2018.04.0137}, url = {files/bibliography/18-019.pdf}, author = {S. W. Salley and Jeffrey E. Herrick and Holmes, Caitlin and Jason W. Karl and Matthew Levi and Sarah McCord and Cornelis Van De Waal and Justin Van Zee} } @proceedings {JRN54860, title = {Testing the ecological site group concept}, volume = {January 28-February 2, 2018}, year = {2018}, month = {01/28/2018}, publisher = {Society for Range Management Meeting Proceedings}, address = {Reno, NV}, abstract = {

The 2016 {\textquotedblleft}Ecological Sites for Landscape Management{\textquotedblright} special issue of Rangelands recommended an update to our thinking of Ecological Sites, suggesting that in our desire to make Ecological Sites more quantitative, we abandoned consideration of Ecological Sites{\textquoteright} spatial context. In response, Ecological Site Groups (ESGs) and associated general state-and-transition modelwere proposed as a framework for describing landscape-level processes occurring across multiple ecological sites, and thus integrating multiple ecological sites from similar landscapes into common behavioral units. We hypothesized that the spatial distribution of ESGs could be predicted using readily available geospatial data due to the theoretical association between ESGs and landscape biophysical properties. Here we test ESG concepts with a spatial modeling framework using machine learning algorithms, a SSURGO modified NASIS point dataset, and a suite of remote sensing-based spatial covariates (e.g., hyper-temporal remote sensing, terrain attributes, climate data, land-cover, and lithology). Our modeling approach was tested on two Major Land Resource Area (MLRA) study areas within the western U.S., representing 6.1 million ha within MLRA 35 and 7.5 million ha within MLRA 42. Results show our approach was effective in mapping ESGs, with a 64\% correct classification based on 1,406 point observations across 8 expertly-defined ESG classes in MLRA 35 and a 75\% correct classification based on 2,626 point observations across 9 expertly-defined ESG classes in MLRA 42. National coverage of the training and covariate data used in this pilot study provides opportunities for a consistent national-scale mapping effort of ESGs.

}, author = {S. W. Salley and Jonathan Maynard and Travis Nauman and Curtis Talbot and Joel R. Brown} } @conference {JRN54811, title = {Assessing quality of citizen scientists{\textquoteright} soil texture estimates to evaluate land potential}, booktitle = {Soil Science Society of America Journal}, year = {2017}, month = {10/2017}, publisher = {Soil Science Society of America}, organization = {Soil Science Society of America}, address = {Tampa, FL}, abstract = {

Texture influences nearly all soil processes and is often the most measured parameter in soil science. Estimating soil texture is a universal and fundamental practice applied by resource scientists to classify and understand the behavior and management of soil systems. While trained soil scientist can accurately estimate soil textural class, percent sand, and percent clay, doubt is often cast on the quality of texture estimates produced by inexperienced citizen scientists. We present analysis of citizen scientist texture-by-feel and laboratory hydrometer derived soil texture classes collected from Namibia, Africa. Participants received minimal training and were provided with the Land Potential Knowledge System (LandPKS) app LandInfo module containing soil texture-by-feel walkthrough instructions. LandPKS is a free and open source mobile and web app that facilitates the collection and recording of georeferenced data on soil properties and site characteristics. Analysis of texture accuracy was compared using confusion matrix analysis to evaluate user and classification accuracy. Classification of sandy and clayey textural classes were most accurate while loamy soil texture classes were poorly predicted. Results showed poor total classifier accuracy (38\%), although classification improved significantly (59\%) when sand and sandy loam categories where combined as misclassification between the sandy texture classes was generally due to soil material containing higher organic matter content. Most incorrect identifications were not considered a serious problem because estimates fell within adjacent textural classes. When combined with LandPKS geotagged smart phone location, our analysis suggests that surface and subsurface texture class estimates were sufficient to match key soil components from the FAO Harmonized World Soil Database and the ISRIC SoilGrids global databases

}, author = {S. W. Salley and Jeffrey E. Herrick} } @conference {JRN54671, title = {Completing the land resource hierarchy}, booktitle = {Society for Range Management}, volume = {Jan 29 - Feb 02, 2017}, year = {2017}, month = {01/2017}, publisher = {Society for Range Management}, organization = {Society for Range Management}, address = {Saint George, Utah}, abstract = {

The Land Resource Hierarchy of the NRCS is a hierarchal landscape classification consisting of resource areas which represent both conceptual and spatially discrete landscape units stratifying agency programs and practices. The Land Resource Hierarchy (LRH) scales from discrete points (soil pedon and vegetation patch) up to broad continental climate zones (Land Resource Regions). \ Inclusion of Ecological Sites into the LRH has renewed interest in strengthening the concepts behind the NRCS{\textquoteright}s landscape classification system which ultimately will lead to revisions of Agriculture Handbook $\#$296 and updates to Major Land Resource Areas. In this paper we identify knowledge gaps and provide recommendations to complete the Land Resource Hierarchy and the subsequent landscape classification mapping updates. For example, one major inconsistency of the LRH occurs at the mid-scales of the hierarchy, specifically the Ecological Site Groups, Land Resource Units, and Major Land Resource Areas. These middle category scales in the hierarchy currently lack strong guidance and policy required for a consistent national and scientifically defensible product. To strengthen the hierarchical model of the LRH, we recommend that respective scales in the hierarchy be defined by ecosystem components at spatial and temporal scales which reflect relevant ecological dynamics. Furthermore, each focal level in the hierarchy should be constrained by only the immediate up-scale resource area (which provides context, significance, and boundary conditions) and subsequently tested by only the immediate down-scale resource area (which gives specificity, explains, and provides mechanisms delineating the resource area). Adopting a combined top-down and bottom-up approach limited to the focal scale of interest is important as ecosystem processes operate at different temporal and spatial scales and often demonstrate cross-scale interactions that a limited scaled-up or scaled-down only system fails to identify.

}, author = {S. W. Salley and Curtis Talbot and Joel R. Brown} } @article {JRN54606, title = {Completing the land resource hierarchy}, journal = {Rangelands}, volume = {38}, year = {2016}, month = {12/2016}, chapter = {313}, abstract = {

The Land Resource Hierarchy of the NRCS is a hierarchal landscape classification consisting of resource areas which represent both conceptual and spatially discrete landscape units stratifying agency programs and practices. The Land Resource Hierarchy (LRH) scales from discrete points (soil pedon and vegetation patch) up to broad continental climate zones (Land Resource Regions). \ Inclusion of Ecological Sites into the LRH has renewed interest in strengthening the concepts behind the NRCS{\textquoteright}s landscape classification system which ultimately will lead to revisions of Agriculture Handbook $\#$296 and updates to Major Land Resource Areas. In this paper we identify knowledge gaps and provide recommendations to complete the Land Resource Hierarchy and the subsequent landscape classification mapping updates. For example, one major inconsistency of the LRH occurs at the mid-scales of the hierarchy, specifically the Ecological Site Groups, Land Resource Units, and Major Land Resource Areas. These middle category scales in the hierarchy currently lack strong guidance and policy required for a consistent national and scientifically defensible product. To strengthen the hierarchical model of the LRH, we recommend that respective scales in the hierarchy be defined by ecosystem components at spatial and temporal scales which reflect relevant ecological dynamics. Furthermore, each focal level in the hierarchy should be constrained by only the immediate up-scale resource area (which provides context, significance, and boundary conditions) and subsequently tested by only the immediate down-scale resource area (which gives specificity, explains, and provides mechanisms delineating the resource area). Adopting a combined top-down and bottom-up approach limited to the focal scale of interest is important as ecosystem processes operate at different temporal and spatial scales and often demonstrate cross-scale interactions that a limited scaled-up or scaled-down only system fails to identify.

}, doi = {10.1016/j.rala.2016.10.003}, url = {files/bibliography/16-028.pdf}, author = {S. W. Salley and Curtis H. Monger and Joel R. Brown} } @conference {JRN54615, title = {Completing the land resource hierarchy}, booktitle = {Soil Science Society of America Annual Meeting}, year = {2016}, month = {11/2016}, publisher = {Soil Science Society of America}, organization = {Soil Science Society of America}, address = {Phoenix, AZ}, abstract = {

The Land Resource Hierarchy of the NRCS is a hierarchal landscape classification consisting of resource areas which represent both conceptual and spatially discrete landscape units stratifying agency programs and practices. The Land Resource Hierarchy (LRH) scales from discrete points (soil pedon and vegetation patch) up to broad continental climate zones (Land Resource Regions). \ Inclusion of Ecological Sites into the LRH has renewed interest in strengthening the concepts behind the NRCS{\textquoteright}s landscape classification system which ultimately will lead to revisions of Agriculture Handbook $\#$296 and updates to Major Land Resource Areas. In this paper we identify knowledge gaps and provide recommendations to complete the Land Resource Hierarchy and the subsequent landscape classification mapping updates. For example, one major inconsistency of the LRH occurs at the mid-scales of the hierarchy, specifically the Ecological Site Groups, Land Resource Units, and Major Land Resource Areas. These middle category scales in the hierarchy currently lack strong guidance and policy required for a consistent national and scientifically defensible product. To strengthen the hierarchical model of the LRH, we recommend that respective scales in the hierarchy be defined by ecosystem components at spatial and temporal scales which reflect relevant ecological dynamics. Furthermore, each focal level in the hierarchy should be constrained by only the immediate up-scale resource area (which provides context, significance, and boundary conditions) and subsequently tested by only the immediate down-scale resource area (which gives specificity, explains, and provides mechanisms delineating the resource area). Adopting a combined top-down and bottom-up approach limited to the focal scale of interest is important as ecosystem processes operate at different temporal and spatial scales and often demonstrate cross-scale interactions that a limited scaled-up or scaled-down only system fails to identify.

}, author = {S. W. Salley and Joel R. Brown and Curtis H. Monger} } @proceedings {JRN54573, title = {From landscape to domain: Soils role in landscape classifications}, year = {2016}, month = {3/2016}, publisher = {International Soil Modeling Consortium (ISMC) Inaugural Workshop}, address = {Austin, TX}, abstract = {Soil landscape classifications are designed to divide landscapes into units with significance for the provisioning and regulating of ecosystem services and the development of conservation plans for natural resources. More specifically, such classifications serve as the basis for stratifying management strategies relevant to any given ecosystem{\textquoteright}s biotic and abiotic properties. The purpose for delineating resource units is to identify geographical areas at different levels of resolution that have similar capabilities and potentials for management. As soil variability is scale-dependent in both space and time, it is important to understand spatial and temporal variables controlling soil and ecosystem function at specific scales of interest. Often soil geographic models at regional and domain scales are based on up-scaled data aggregated from the downscale soil properties. Yet at macroscales, soil ecosystems are controlled by macroclimatic properties that control daily and seasonal fluxes of energy and moisture, such as latitude (variability of soil energy), distance from the sea (continentality or oceanic influences), and elevation. It is only at mesoscale and microscale levels that landform properties (such as geology and topography) modify macroclimates by regulating the intensity of other key factors important to soil formation. Although results are preliminary, we show that landscape classification models identifying regional and domain scaled soil systems should be based on the more homogeneous macroclimate variables, as these systems lay above the local modifying influences of landforms. }, author = {S. W. Salley and Joel R. Brown} } @article {JRN54562, title = {A long-term analysis of the historical dry boundary for the Great Plains of North America: Implications of climatic variability and climatic change on temporal and spatial patterns in soil moisture}, journal = {Geoderma}, volume = {274}, year = {2016}, month = {07/2016}, pages = {104-113}, chapter = {104}, abstract = {

The boundary between the humid eastern and the arid western United States is of great economic interest and historic intrigue. Areas to the east of this geographic boundary have historically enjoyed more favorable rainfall, fertile soil, and reliable surface water, permitting conventional agriculture to flourish over a surprisingly large percentage of the eastern Great Plains. The expansion of population and agriculture during the nineteenth century across the Great Plains of North America tested the extent to which row crop agriculture could be successful in areas where year-to-year rainfall was unreliable. In this paper, we have quantified the historic variability of soil moisture and hydrologic conditioning resulting from climatic variability in the Great Plains by showing regions that historically demonstrate reliable precipitation, identifying an extent for the arid regions of the United States based on modeled soil moisture. We demonstrate that long-term climate and climate variability are reflected in the depth and concentration of the calcic soil properties. From a practical standpoint, our work suggests that soils with higher water holding capacity can dampen periodic short-term rainfall deficits likewise soils with lower water holding capacity can exhibit edaphic drought during otherwise normal years.

}, keywords = {Calcic soils, climatic variability, Newhall Simulation Model, North American Great Plains, soil moisture}, doi = {10.1016/j.geoderma.2016.03.020}, url = {/files/bibliography/16-009.pdf}, author = {S. W. Salley and Sleezer, Richard and Bergstrom, Robert and Martin, Patrick and E. Kelly} } @proceedings {JRN54572, title = {A method for testing land resource area concepts}, year = {2016}, month = {01/2016}, publisher = {Society for Range Management}, address = {Corpus Christi, TX}, abstract = {Land Resource Units (LRUs) are defined by the National Soil Survey Handbook as aggregations of soil map units and subunits of Major Land Resource Areas (MLRAs). In the USDA NRCS Land Resource Hierarchy, LRUs are defined as the level between MLRAs and STATSGO and are mapped at 1:1 million scale. They also function as an important framework for the development of Ecological Site (ES) concepts. While the art and science of resource area mapping has advanced significantly in the past several decades, NRCS LRU regionalizations have typically lacked suitable scientific foundations in defining resource area concepts. With the recently adopted Provisional Ecological Site initiative (intended to complete initial inventory of ES in the contiguous U.S. by 2020), a pressing need has risen to stratify ES concepts by a practical and functional LRA. Because resource areas, such as Major Land Resource Areas (MLRA), are rarely discrete physical entities{\textquoteright} often being conceptualizations reflecting perceived biases from the mapper{\textquoteright}it is important that resource areas implement rule-based procedures to test LRU concepts and geography. Here we present a methodology suitable for testing resource area boundary concepts and provide examples across the Continental U.S. }, author = {S. W. Salley and Curtis Talbot and Joel R. Brown} } @article {JRN54518, title = {The Natural Resources Conservation Service land resource hierarchy and ecological sites}, year = {2016}, abstract = {Resource areas of the NRCS have long been important to soil geography. At both regional and landscape scales, resource areas are used to stratify programs and practices based on geographical areas where resource concerns, problems, or treatment needs are similar. However, the inability to quantifiably delineate and classify resource area boundaries hinders communication across the NRCS and federal agencies. Without strong standards delineating geography and concepts, resource areas become less scientifically defensible and inconsistent in addressing similar resource management issues. Furthermore, with continued development of Ecological Site concepts, there is a renewed interest in understanding the relationships between resource areas and Ecological Sites. In this paper we: (i) review the concepts and history of landscape classifications leading to the predominant regional classification systems used by federal agencies in the United States, (ii) propose strengthening the NRCS{\textquoteright}s Land Resource Hierarchy by building resource area concepts on nesting principles of subdivision instead of aggregation, and (iii) place ecological site concepts as a nested resource area within the Land Resource Hierarchy.}, doi = {10.2136/sssaj2015.05.0305}, url = {/files/bibliography/16-005.pdf}, author = {S. W. Salley and Curtis Talbot and Joel R. Brown} } @conference {JRN54413, title = {Historical perspectives and recommendations for revision of Agricultural Handbook 296}, booktitle = {2015 National Cooperative Soil Survey National Conference}, year = {2015}, month = {06/2015}, address = {Duluth, MN}, abstract = {

Major Land Resource Areas (MLRAs) are designed to support the development and coordination of soil and water conservation programs by the NRCS, with the primary document describing MLRA being Agricultural Handbook $\#$296 (1965, 1978, 1981, and 2006). While the most recent edition of the handbook was released in 2006, many of the geographic boundaries consistently mimic historic map products of generalized regional and national soil-survey maps (some dating back to the 1930s). Furthermore, the underlying concepts and technologies used to build MLRA geography predate adoption of present-day ecological approaches to resource management and conservation. Thus a need has arisen to better develop and define the concepts allowing for {\textquotedblleft}unique and defensible{\textquotedblright} MLRA geography against neighboring MLRAs. For example, an MLRA must be confined within a physiographic province, contain unique ecological sites/soil map units from its adjoining resource areas, and should incorporate advances in soil climate modeling and data-driven digital soil mapping techniques into MLRA concepts. In preparation for revising the Agricultural Handbook $\#$296, the National Ecological Site Team at the Jornada Experimental Range has underway the compiling of an extensive geographic database of land use, elevation, topography, climate, water resources, potential natural vegetation, and soils. Our approach to developing data-driven MLRA is to use modern geospatial techniques in order to develop a more robust approach to resource area concepts. Our presentation will present the historical development of MLRA concepts and provide recommendations for a national framework in which to create more uniform standards and definitions of MLRA relevant to present day resource and conservation needs.

}, author = {S. W. Salley and Peg Gronemeyer and Curtis Talbot and Joel R. Brown} } @article {JRN54426, title = {Historical Perspectives and Recommendations for revision of the Agricultural Handbook 296 }, year = {2015}, month = {03/2015}, abstract = {

While the most recent edition of Agriculture Handbook 296 was released in 2006, many of the geographic boundaries consistently mimic historic map products of generalized regional and national soil-survey maps (some dating back to the 1930s). Furthermore, the underlying concepts and technologies used to build MLRA geography predate adoption of present-day ecological approaches to resource management and conservation. The National Ecological Site Team at the Jornada has begun compiling a national database of physiography, geology, climate, water resources, soils, and land-use to aid in development a data-driven approach to MLRA. We \ will present the general historical development of MLRA and provide recommendations for revisions of Agricultural Handbook 296.\  www.youtube.com/watch?v=uVTegM8vk5I.

}, author = {S. W. Salley and Curtis Talbot and Joel R. Brown} } @conference {50141, title = {Impact of oil and gas infrastrucure development in La Manga Canyon, NM}, booktitle = {62nd Society for Range Management Annual Meeting}, year = {2009}, month = {02/2009}, pages = {2050-11}, publisher = {Society for Range Management}, organization = {Society for Range Management}, address = {Alb., NM}, abstract = {

La Manga Canyon is a small watershed (~20km2) in the San Juan Basin that has historically been developed for natural gas and recently for coal bed methane. Since gas production began in the 1940s, an extensive network of dirt roads have transected the watershed, providing access to well sites. There are currently eleven and a half wells per square mile with sometimes multiple wells in each well-pad. Land degradation due to energy extraction and historic livestock grazing make this area a good model for similar Great Basin landscapes, particularly in the understanding of soil and vegetation dynamics in high impact areas. Remote sensing was conducted from aerial photography taken in 1955, 1981, 1997, 2005 and 2006 to verify historic extent of well-pads, roads, and pipelines. Well-pads were digitized manually and roads / pipelines were remotely sensed with a supervised classification. Temporal vegetation change in the watershed was assessed at the ecological site level. Organic carbon was also estimated from soil cores (top 10cm) collected from roads, well-pads, and \“undisturbed\” vegetated areas within each ecological site. Linear disturbances increased from 100 to 250 ha between 1955 and 2006; total well-pad area increased from 12 to 72 ha over the same period. Roadless volume decreased by almost 50\%. Tree cover (primarily pinyon-juniper) increased along north facing slopes and decreased on south, west and east slopes. Herbaceous vegetation shifted to annual grasses. Vegetation dynamics in this watershed have been overwhelmed by energy infrastructure.

}, keywords = {gas, infrastructure, La Manga Canyon, NM, oil, SRM}, author = {S. W. Salley and Joel R. Brown} }